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A Hybrid NLP and ML Approach to Fake Review Classification

  • Unique Paper ID: 173829
  • Volume: 11
  • Issue: 10
  • PageNo: 1747-1750
  • Abstract:
  • This study focuses on the design and development of a Fake Review Detection System (FRDS) using concepts like Natural Language Processing (NLP) and Machine Learning (ML) to improve the credibility of online reviews. The aim of the system is to identify and filter fraudulent reviews by considering linguistic patterns, sentiment, and behavioural cues, ensuring trustworthiness for both consumers and businesses. By integrating IP tracking for geolocation analysis, the FRDS improves detection accuracy by correlating review origins with user activity. The model is trained on a variety of datasets to enhance its adaptability across different review platforms, incorporating continuous feedback to refine detection algorithms and stay aligned with emerging deceptive strategies [1].
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 10
  • PageNo: 1747-1750

A Hybrid NLP and ML Approach to Fake Review Classification

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